Return type
DeepLake
persist() → None[source]#
Persist the collection.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.docstore.document.Document][source]#
Return docs most similar to query.
class langchain.vectorstores.ElasticVectorSearch(elasticsearch_url: str, index_name: str, embedding: langchain.embeddings.base.Embeddings)[source]#
Wrapper around Elasticsearch as a vector database.
Example
from langchain import ElasticVectorSearch
elastic_vector_search = ElasticVectorSearch(
    "http://localhost:9200",
    "embeddings",
    embedding
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.elastic_vector_search.ElasticVectorSearch[source]#
Construct ElasticVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in the Elasticsearch instance.
Adds the documents to the newly created Elasticsearch index.
This is intended to be a quick way to get started.
Example
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
    texts,
    embeddings,